Credit Risk Modeller at Inbank

Tallinn, , Estonia -

Full Time

Start Date

Immediate

Expiry Date

20 Jul, 26

Salary

0.0

Posted On

21 Apr, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Credit risk modelling, IFRS 9, Expected Credit Loss, Statistical modelling, SQL, Excel, R, Financial reporting, Data analysis, Risk assessment, Regulatory framework, Communication

Industry

Banking

Description

Inbank is a financial technology company with an EU banking license that connects merchants, consumers, and financial institutions on its next-generation embedded finance platform. Our financing solutions are embedded seamlessly into the shopping journey of 6,000+ retailers. This helps our merchant partners grow their businesses, while end customers benefit from a frictionless shopping experience wherever they are. With a focus on innovation and growth, we are looking for talented people to join our team of 440+ working across our offices in Estonia, Latvia, Lithuania, Poland, and the Czech Republic. Due to our growth ambitions, we seek a talented Credit Risk Modeller to join our Tallinn office. As a Credit Risk Modeler, you will play an important role in developing and enhancing credit risk models, with a strong focus on IFRS 9 Expected Credit Loss (ECL) methodologies, calculations, and framework development across all Inbank markets. You will directly contribute to portfolio performance, risk assessment, and the accuracy of financial reporting. In this role, you’ll be one of the key contributors within our Credit Risk Control team - tackling complex challenges, sharing ideas, and working alongside talented professionals to keep our credit portfolio strong, balanced, and future-ready. What will you be doing? Developing and maintaining IFRS 9 Expected Credit Loss (ECL) models, including staging, segmentation, and macroeconomic overlays. Supporting provisioning processes and financial reporting activities. Monitoring model performance and implementing enhancements to improve existing models. Contributing to the development and documentation of IFRS 9 methodologies, ensuring alignment with internal standards and regulatory requirements. Collaborating with risk, data, and engineering teams on ad hoc credit risk modelling projects. Ideally, you would have: 1-2 years of previous experience in credit risk modelling (experience in IFRS 9 Expected Credit Loss (ECL) frameworks is an advantage) or other statistical modelling related field. Solid knowledge of statistical methods and practical experience with modelling techniques. Proficiency in SQL & Excel (experience with R or similar modelling tools is a plus). Familiarity with the regulatory framework in the credit risk domain is an advantage. Strong communication skills in English & Estonian, with the ability to translate complex concepts into clear, actionable insights What you’ll get in return? A competitive salary tailored to your experience, along with a comprehensive benefits package. Wellbeing support through sports compensation or additional health insurance to help you stay active and healthy. Extra vacation days after your third year, giving you more time to rest and recharge. A 6-week paid sabbatical after four years, recognising strong performance and long-term contribution. A dynamic and inspiring work environment where you’re encouraged to grow and take ownership of your work. Flexibility through a hybrid and autonomous way of working, built on trust and accountability. The opportunity to collaborate with talented international colleagues across multiple markets. Regular team events and additional perks that make work more enjoyable and help celebrate successes together. Sounds like a fit? Apply, and our recruitment team will be in touch. If you’re unsure but curious, apply anyway - we’re happy to explore together.

Responsibilities

You will develop and maintain IFRS 9 Expected Credit Loss models, including staging, segmentation, and macroeconomic overlays. Additionally, you will support provisioning processes and collaborate with cross-functional teams on credit risk modelling projects.